Analysis of the paper: “Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely”

This paper conducts a comprehensive survey on how to optimize the integration of external data into large language models (LLMs) to address specific user queries. The authors categorize these queries into four levels, identify key challenges for each category, and suggest corresponding methodologies to enhance the functionality of data-augmented LLM applications.

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paper citation: Zhao, Siyun, Yuqing Yang, Zilong Wang, Zhiyuan He, Luna K. Qiu, and Lili Qiu. “Retrieval Augmented Generation (RAG) and Beyond: A Comprehensive Survey on How to Make your LLMs use External Data More Wisely.” arXiv preprint arXiv:2409.14924 (2024).

Summary

The research presents a detailed examination of how large language models (LLMs) can be augmented with external data to improve their performance on real-world tasks.

By exploring various methods, such as Retrieval-Augmented Generation (RAG) and…